Applications of Metaheuristic Algorithms in Solar Air Heater Optimization: A Review of Recent Trends and Future Prospects by Jean De Dieu Niyonteze, Fumin Zou, Godwin Norense Osarumwense Asemota, Walter Nsengiyumva, Noel Hagumimana, Longyun Huang, Aphrodis Nduwamungu, Samuel Bimenyimana

“…Therefore, this paper clearly shows that the use of all six proposed metaheuristic algorithms results in significant efficiency improvements through the selection of the optimal design set and operating parameters for SAHs. …”
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Sustainable supply chain: An optimization and resource efficiency in additive manufacturing for automotive spare part by Divya Chauhan, Anubhav Pratap Singh, Anand Chauhan, Ritu Arora

“…The incorporation of additive manufacturing within the automotive supply chain holds the potential to improve operational efficiency and generate significant cost savings while enhancing sustainability. …”
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Adaptive k-means clustering algorithm based on grid and domain centroid weight by Wei CAI, Guangyu SUN, Fei YANG, Bo YAO, Chuanxian LI

“…Furthermore, when the clustering accuracy exceeded 99％ compared to the k-means algorithm, the computational efficiency was significantly improved. …”
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Charging path optimization in mobile wireless rechargeable sensor networks by Quanlong NIU, Riheng JIA, Minglu LI

“…The wireless power transfer technique is promising in solving the energy bottleneck of sensor nodes in wireless sensor networks, which can thus prolong the network lifetime or even maintain sustainable network operations.Most existing works focused on optimizing the static chargers’ deployment or mobile chargers’ charging path for static sensor nodes with fixed sensor node positions, ignoring the scenario with mobile sensor nodes.Thus, design and optimize the charging path of a mobile charger was studied for dynamic wireless sensor networks with mobile sensor nodes, to maximize the charging utility within a finite time horizon, that is, the charger can encounter as more sensor nodes as possible in a limited time and charge them.Notice that the mobile charger may stop to simultaneously charge multiple nodes within its charging range during its charging tour.The proposed charging path optimization problem was proven to be an APX-hard problem.Then, based on the constructed directed acyclic graph using discretization method, a layer-wise pruning algorithm based on the backtracking method was proposed.The proposed algorithm took the solution generated by the greedy algorithm as the benchmark and searched the optimal charging path under a fixed time division by layer-wise pruning.Simulation results show that the proposed algorithm can effectively improve the charging utility .…”
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Charging path optimization in mobile wireless rechargeable sensor networks by Quanlong NIU, Riheng JIA, Minglu LI

“…The wireless power transfer technique is promising in solving the energy bottleneck of sensor nodes in wireless sensor networks, which can thus prolong the network lifetime or even maintain sustainable network operations.Most existing works focused on optimizing the static chargers’ deployment or mobile chargers’ charging path for static sensor nodes with fixed sensor node positions, ignoring the scenario with mobile sensor nodes.Thus, design and optimize the charging path of a mobile charger was studied for dynamic wireless sensor networks with mobile sensor nodes, to maximize the charging utility within a finite time horizon, that is, the charger can encounter as more sensor nodes as possible in a limited time and charge them.Notice that the mobile charger may stop to simultaneously charge multiple nodes within its charging range during its charging tour.The proposed charging path optimization problem was proven to be an APX-hard problem.Then, based on the constructed directed acyclic graph using discretization method, a layer-wise pruning algorithm based on the backtracking method was proposed.The proposed algorithm took the solution generated by the greedy algorithm as the benchmark and searched the optimal charging path under a fixed time division by layer-wise pruning.Simulation results show that the proposed algorithm can effectively improve the charging utility .…”
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A Method for Service Function Chain Migration Based on Server Failure Prediction in Mobile Edge Computing Environment by Joelle Kabdjou, Norihiko Shinomiya

“…Using a Long Short-Term Memory (LSTM) algorithm optimized by Super SAPSO (Simulated Annealing Particle Swarm Optimization), the model forecasts server failures with improved accuracy, reducing False Alarm Rates and improving Failure Detection Rates. …”
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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. …”
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Intelligent bulk cargo terminal scheduling based on a novel chaotic-optimal thermodynamic evolutionary algorithm by Shida Liu, Qingsheng Liu, Li Wang, Xianlong Chen

“…The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. …”
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Optimal Placement of Leakage Sensors in Urban Gas Networks Based on an Ant Colony Algorithm and System Clustering by Zhewen Sui, Xiaobing Yuan, Baoping Cai, Fangqi Ye, Qingqing Duan, Zhiqiang Zhao, Xiaoyan Shao, Xin Zhou, Zhiming Hu

“…In urban gas network leakage monitoring, the optimized placement of sensors plays a pivotal role in ensuring public safety and minimizing system maintenance costs. …”
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An Effective HS-CG Update: Avoiding Oscillations in Nonlinear Optimization by Isam H. Halil

“… In this research, we introduce a modified direction update formula to improve the descent features of iterative optimization, therefore presenting a fresh approach to the Conjugate Gradient (CG) method. …”
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Integration of electric vehicle charging stations with distributed generation using multi-objective metaheuristic optimization by Aya Desoky Gaber, E.M. Abdallah, M.I. Elsayed, Ahmed Abdelbaset

“…This paper uses a multi-objective optimization approach metaheuristic algorithm, specifically the Whale Optimization Algorithm (WOA), Zebra Optimization Algorithm (ZOA), and Puma Optimization Algorithm (POA), to determine the optimal size and placement of DG units in the presence of EVCS. …”
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Usage of the optimal fuzzy system models on chloride diffusion of concrete by Shuai Sun

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Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm by Aseel Bennagi, Obaida AlHousrya, Daniel T. Cotfas, Petru A. Cotfas

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RELIABILITY OPTIMIZATION DESIGN ON SHEARER’S RANGING ARM GEAR TRANSMISSION SYSTEM by LI FengJiang, ZHANG Jie, LIAO YingHua, LI QingChen

“…The results show that this method of optimization can improve gear transmission’s reliability,the volume and weight of gear transmission system are reduced too,which is beneficial to achieving products lightweight and reduceing production cost.…”
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Mechanical and Civil Engineering Optimization with a Very Simple Hybrid Grey Wolf—JAYA Metaheuristic Optimizer by Chiara Furio, Luciano Lamberti, Catalin I. Pruncu

“…Furthermore, SHGWJA was very robust: (i) in most cases, SHGWJA obtained a 0 or near-0 standard deviation and all optimization runs practically converged to the target optimum solution; (ii) standard deviation on optimized cost was at most 0.0876% of the best design; (iii) the standard deviation on function evaluations was at most 35% of the average computational cost. …”
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Smart village construction and college students’ skills entrepreneurship path based on LSTM and simulated annealing algorithm by Xueli Dong

“…At the same time, in order to improve the success rate and profitability of college students’ skill entrepreneurship projects, a series of simulation experiments were carried out with the help of simulated annealing algorithm, and an optimal path taking into account costs and benefits was determined through the simulation and comparison of different entrepreneurial models. …”
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A Data Resource Trading Price Prediction Method Based on Improved LightGBM Ensemble Model by Wan Nie, Bingliang Shen, Desheng Li

“…To address the key challenges of limited practical application, high implementation difficulty, and poor generalization capability in existing theoretical models for data resource pricing, this study employs generative adversarial network (GAN) to augment the dataset and constructs a DRV-LightGBM model based on a Bayesian parameter optimization algorithm that maximizes the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to predict data resource transaction prices and provide post-hoc explanations for the prediction model. …”
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Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Maziani Sabudin

“…Both were used together with binary cuckoo optimization algorithm BCOA (BCOA-MI and BCOA-EI). The target is to improve classification performance (reduce the error rate and computational complexity) on eight datasets with varying degrees of complexity. …”
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Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems by Yanling Chen, Jingyi Wei, Tao Luo, Jie Zhou

“…In practical applications, optimizing vehicle capacity allocation can significantly improve route optimization performance and service coverage. …”
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Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review by Yanzhao Yang, Jian Wang, Xinyu Guo, Xinyu Yang, Wei Qin

“…Additionally, we discuss verification techniques, including direct and indirect methods, to assess authenticity improvements. Experimental results demonstrate the effectiveness of these techniques in enhancing perception algorithm performance. …”
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